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The Dynamic Window Approach (DWA) is a popular method for Unmanned Aerial Vehicle (UAV) navigation and localization in unknown environments. It combines Dynamic Programming (DP) with a Probabilistic Route Mapping (PRM) algorithm to provide efficient path planning and obstacle avoidance. DWA can handle a wide range of obstacles, including dynamic and uncertain ones, making it highly reliable. The approach utilizes dynamic programming to compute the optimal path based on the UAV's current state and the known environment. It also employs a hybrid probabilistic route mapping algorithm to estimate the location and movement of unknown obstacles. By combining these techniques, DWA enables the UAV to navigate through complex environments efficiently. One of DWA's key strengths is its ability to handle non-holonomic constraints, such as the limited turning radius of a mobile UAV. It achieves this by defining a dynamic window that determines the feasible set of motions for the UAV at any given time and adjusts the path accordingly. Compared to other popular methods like the Rapidly Exploring Random Trees (RRT) algorithm, DWA outperforms in terms of path planning and obstacle avoidance. It overcomes the limitations imposed by the size of autonomous mobile UAVs by considering the relationship between the robot's dimensions and obstacles in the open space. To enhance sensing and prediction of the surroundings, a laser range finder is utilized in DWA, particularly to handle curved structures or box-canyon formations. This, along with the Dynamic Programming (DP) algorithm, optimizes the path by considering the gathered information. The proposed approach addresses the local minima problem through a strategy to identify the effective path region. Theoretical studies and simulations demonstrate the efficiency and superiority of DWA. In summary, the Dynamic Window Approach is an efficient method for UAV navigation and localization in unknown environments. By combining dynamic programming, probabilistic route mapping, and considering non-holonomic constraints, it provides reliable path planning and obstacle avoidance. Its ability to handle various obstacles, including dynamic ones, sets it apart from other methods, making it highly valuable for UAV applications.

Home indoor environments are characterized by alternating open spaces and obstacle-cluttered regions, which pose critical challenges to the autonomous navigation of home service robots. Existing hybrid path planning algorithms generally suffer from three core limitations: low global search efficiency, weak global-local planning coordination, and poor dynamic scene adaptability. To tackle these issues, this paper presents a novel hierarchical path planning framework combining an enhanced Jump Point Search (JPS) and a fuzzy-optimized Dynamic Window Approach (DWA). In the global planning layer, an adaptive Octile heuristic JPS based on local obstacle density is designed to reduce redundant node expansion and accelerate global path search, with a bounded suboptimality guarantee. To bridge global and local planning, a look-ahead distance-based dynamic waypoint selection strategy is developed to match the optimal waypoint in real time according to the robot’s motion state and environmental complexity, enabling seamless coordination between global path guidance and local trajectory generation. In the local planning layer, a fuzzy logic controller is introduced to dynamically tune the weights of the DWA trajectory evaluation function, which significantly improves the robot’s dynamic obstacle avoidance capability and motion smoothness. Comparative simulation experiments verify that the proposed method not only outperforms the conventional hybrid path planning algorithm, reducing expanded nodes by 68.09% and global planning time by 52.94%, while improving dynamic obstacle avoidance success rate by 31.43% and overall navigation efficiency by 23.95%, it also achieves better comprehensive navigation performance than the widely adopted PSO-DWA comparison algorithm. The proposed framework shows superior comprehensive performance and is well suited for the indoor autonomous navigation of home service robots.

This paper proposes a formation generation algorithm and formation obstacle avoidance strategy for multiple unmanned surface vehicles (USVs). The proposed formation generation algorithm implements an approach combining a virtual structure and artificial potential field (VSAPF), which provides a high accuracy of formation shape keeping and flexibility of formation shape change. To solve the obstacle avoidance problem of the multi-USV system, an improved dynamic window approach is applied to the formation reference point, which considers the movement ability of the USV. By applying this method, the USV formation can avoid obstacles while maintaining its shape. The combination of the virtual structure and artificial potential field has the advantage of less calculations, so that it can ensure the real-time performance of the algorithm and convenience for deployment on an actual USV. Various simulation results for a group of USVs are provided to demonstrate the effectiveness of the proposed algorithms.

Bat algorithm has disadvantages of slow convergence rate, low convergence precision and weak stability. In this paper, we designed an improved bat algorithm with a logarithmic decreasing strategy and Cauchy disturbance. In order to meet the requirements of global optimal and dynamic obstacle avoidance in path planning for a mobile robot, we combined bat algorithm (BA) and dynamic window approach (DWA). An undirected weighted graph is constructed by setting virtual points, which provide path switch strategies for the robot. The simulation results show that the improved bat algorithm is better than the particle swarm optimization algorithm (PSO) and basic bat algorithm in terms of the optimal solution. Hybrid path planning methods can significantly reduce the path length compared with the dynamic window approach. Path switch strategy is proved effective in our simulations.

Intelligent mobile robots play an important role in the green and efficient operation of warehouses and have a significant impact on the natural environment and the economy. Path planning technology is one of the key technologies to achieve intelligent mobile robots. In order to improve the pickup efficiency and to reduce the resource waste and carbon emissions in logistics, we investigate the robot path optimization problem. Under the guidance of the sustainable development theory, we aim to achieve the goal of environmental social governance by shortening and smoothing robot paths. To improve the robot’s ability to avoid dynamic obstacles and to quickly solve shorter and smoother robot paths, we propose a fusion algorithm based on the improved genetic algorithm and the dynamic window approach. By doing so, we can improve the efficiency of warehouse operations and reduce logistics costs, whilst also contributing to the realization of a green supply chain. In this paper, we implement an improved fusion algorithm for mobile robot path planning and illustrate the superiority of our algorithm through comparative experiments. The authors’ findings and conclusions emphasize the importance of using advanced algorithms to optimize robot paths and suggest potential avenues for future research.

In complex maritime environments, the safety of path planning for Unmanned Surface Vehicles (USVs) remains a significant challenge. Existing methods for handling dynamic obstacles often suffer from inadequate predictability and generate non-smooth trajectories. To address these issues, this paper proposes a reliable hybrid path planning approach that integrates a reinforcement learning-enhanced A* algorithm with an improved Dynamic Window Approach (DWA). Specifically, the A* algorithm is augmented by incorporating a dynamic five-neighborhood search mechanism, a reinforcement learning-based adaptive weighting strategy, and a path post-optimization procedure. These enhancements collectively shorten the path length and significantly improve trajectory smoothness. While ensuring that the global path avoids dynamic obstacles smoothly, a Kalman Filter (KF) is integrated into the Long Short-Term Memory (LSTM) network to preprocess historical data. This mechanism suppresses transient outliers and stabilizes the trajectory prediction of dynamic obstacles. Moreover, the evaluation function of the DWA is refined by incorporating the International Regulations for Preventing Collisions at Sea (COLREGs) constraints, enabling compliant navigation behaviors. Simulation results in MATLAB demonstrate that the enhanced A* algorithm better conforms to the kinematic model of the USVs. The improved DWA significantly reduces collision risks, thereby ensuring safer navigation in dynamic marine environments.

Traditional path planning algorithms often encounter challenges in complex dynamic environments, including local optima, excessive path lengths, and inadequate dynamic obstacle avoidance. Thus, the development of innovative path planning algorithms is essential. This article addresses the challenges of mobile robot path planning in complex environments, where traditional methods often converge to local optima, leading to suboptimal path lengths, and struggle with dynamic obstacle avoidance. To overcome these limitations, we propose an integrated algorithm, the enhanced sparrow search algorithm combined with the dynamic window approach (ESSA-DWA). The algorithm first utilizes ESSA for global path planning, followed by local path planning facilitated by the DWA. Specifically, ESSA incorporates Tent chaotic initialization to enhance population diversity, effectively mitigating the risk of premature convergence to local optima. Moreover, dynamic adjustments to the inertia weight during the search process enable an adaptive balance between exploration and exploitation. The integration of a local search strategy further refines individual updates, thereby improving local search performance. To enhance path smoothness, the Floyd algorithm is employed for path optimization, ensuring a more continuous trajectory. Finally, the combination of ESSA and DWA uses key nodes from the global path generated by ESSA as reference points for the local planning process of DWA. This approach ensures that the local path closely follows the global path while also enabling real-time dynamic obstacle detection and avoidance. The effectiveness of the algorithm has been validated through both simulations and practical experiments, offering an efficient and viable solution to the path planning problem.

This article is concerned with the path planning of mobile robots in dynamic environments. A new path planning strategy is proposed by integrating the improved ant colony optimization (ACO) and dynamic window approach (DWA) algorithms. An improved ACO is developed to produce a globally optimal path for mobile robots in static environments. Through improvements in the initialization of pheromones, heuristic function, and updating of pheromones, the improved ACO can lead to a shorter path with fewer turning points in fewer iterations. Based on the globally optimal path, a modified DWA is presented for the path planning of mobile robots in dynamic environments. By deleting the redundant nodes, optimizing the initial orientation, and improving the evaluation function, the modified DWA can result in a more efficient path for mobile robots to avoid moving obstacles. Some simulations are conducted in different environments, which confirm the effectiveness and superiority of the proposed path planning algorithms.

In response to the shortcomings of the traditional A-star algorithm, such as excessive node traversal, long search time, unsmooth path, close proximity to obstacles, and applicability only to static maps, a path planning method that integrates an adaptive A-star algorithm and an improved Dynamic Window Approach (DWA) is proposed. Firstly, an adaptive weight value is added to the heuristic function of the A-star algorithm, and the Douglas–Pucker thinning algorithm is introduced to eliminate redundant points. Secondly, a trajectory point estimation function is added to the evaluation function of the DWA algorithm, and the path is optimized for smoothness based on the B-spline curve method. Finally, the adaptive A-star algorithm and the improved DWA algorithm are integrated into the fusion algorithm of this article. The feasibility and effectiveness of the fusion algorithm are verified through obstacle avoidance experiments in both simulation and real environments.

Planning the movement path of a robot is crucial to ensure it reaches the target area smoothly. Existing methods tend to fall into local optima, have low accuracy in route calculation, and fail to effectively avoid obstacles. To address these issues, this study introduces the Sparrow Search Algorithm and Fuzzy Control, as well as the Dynamic Window Approach, to optimize Proportional-Integral-Derivative control and Batch Informed Trees, respectively. Based on these two optimization algorithms, a robot trajectory planning model is proposed, and its feasibility and reliability are demonstrated through comparative experiments. In standardized 50m×50m grid environments with 20%-30% obstacle density and dynamic obstacles, 30 independent simulation runs were conducted. Comparative analysis with RRT*, Ant Colony Optimization (ACO), and Genetic Algorithm (GA) demonstrates that the proposed model achieves a success rate of 95.5%, a high accuracy rate of 99.4%, and a low accuracy error rate of 0.0011%. The locally optimal route length planned by the model is 12.6m, while the global average optimal route length is reduced to 21.2m, significantly outperforming the comparison models. These findings demonstrate that the proposed model has strong trajectory planning capabilities, minimal error, and shorter routes, enabling the robot to respond correctly to external environments in a timely manner and complete tasks effectively even in complex dynamic conditions.